Abstract
Despite stringent procedures to secure the best HLA-matching between donors and recipients, life-threatening complications continue to occur after hematopoietic stem cell transplantation (HSCT). Studying single nucleotide polymorphism (SNP) in genes encoding costimulatory molecules could help identify patients at risk for post HSCT complications. In a stepwise approach we selected SNPs in key costimulatory molecules including CD274, CD40, CD154, CD28 and TNFSF4 and systematically analyzed their association with post HSCT outcomes. Our discovery cohort analysis of 1157 HLA-A,B,C DRB1 and DQB1 matched cases, found that patients with donors homozygous for the C variant of rs10912564 in TNFSF4 (48%) had better disease-free survival (P=.029) and overall survival (P=.009) with less treatment related mortality (P=.006). Our data demonstrate the TNFSF4C variant had a higher affinity for the nuclear transcription factor Myb and increased percentage of TNFSF4 positive B cells following stimulation compared to CT or TT. However, these associations were not validated in a more recent cohort, potentially due to changes in standard of practice or absence of a true association. Given the discovery cohort, functional data and importance of TNFSF4 in infection clearance, TNFSF4C may associate with outcomes, and warrants future studies.
Introduction
Although hematopoietic stem cell transplantation (HSCT) has become the gold standard therapy for hematological disorders and malignancies, the full therapeutic potential of HSCT has been limited due to its complications. While the standardization of pre-transplant donor-recipient matching for human leukocyte antigens (HLA) has greatly improved post HSCT outcomes, the mortality rate still remains twice that of the general population even in patients who survived two or more years following an allogenic-HSCT.1 This excess mortality is caused by a number of complications post transplantation such as graft versus host disease, infection and relapse of the primary disease.1,2 There is growing evidence supporting the importance of genetic variability outside the HLA system that is contributing to the heterogeneity in HSCT outcomes.3–5 Responses to alloantigen, tumor surveillance and infectious complications post HSCT rely heavily on a functional immune system. Costimulatory molecules represent an essential regulatory component of the immune system, which may be functionally affected by gene polymorphisms.5,6 For T cell activation two activation signals are required. The first signal occurs after the T cell receptor and a coreceptor interact with the antigen peptide presented on a major histocompatibility (MHC) molecule by an antigen-presenting cell (APC). The second positive costimulatory signal occurs when one or more T cell surface receptors engage with their specific ligands on APCs.7–10 Conversely, negative costimulatory molecules, decrease T cell proliferation and cytokine production, promote T cell anergy or apoptosis, and induce the activity of regulatory T cells.11 For this study we selected SNPs based on a stepwise criteria from co-stimulatory genes, which have an established association in human and animal HSCT outcomes.12–14 SNPs needed to have a strong linkage disequilibrium to additional tagged SNPs within their gene to maximize a subset of informative SNPs. Each SNP required an adequate minor allelic frequency of polymorphism above 5% to ensure the least common variant was present in our population. Lastly, every SNP required enough subjects in our population to detect a significant association at 80% power. From these guidelines we created a panel of SNPs with a high likelihood of discovering an association in our HSCT population. Determining novel predictors could help develop a pre transplant matching system to identify patients at risk and modify immunosuppressive regimens based on their risk assessment.
The tumor necrosis factor ligand superfamily member 4 (TNFSF4), and tumor necrosis factor receptor superfamily member 4 (TNFRSF4) pathway represents one of the key positive costimulatory signals required for cell activation. TNFRSF4 is present on both activated CD4+ and CD8+ T cells, and its cognate ligand, TNFSF4, is expressed on dendritic cells, B cells, and activated endothelial cells.15 Signaling through the TNFSF4 / TNFRSF4 pathway facilitates T helper type 2 differentiation, enhances effector CD8+ T cell memory commitment and promotes cytokine production.16,17 Gene polymorphisms in TNFSF4 have been associated with atherosclerosis and systemic lupus erythematosus.18–20 These studies postulate that TNFSF4 is a major component in the T cell-APC interaction leading to activation of immune cells to produce proinflammatory cytokines and chemokines resulting in active disease. The role of TNFSF4 in determining the post HSCT outcomes remains to be explored.
Infectious complications are a contributing source of severe morbidity and non-relapse related mortality in unrelated donor allogeneic HSCT.21 They account for a higher percentage of mortality compared to GVHD in both HLA-identical sibling and unrelated donor transplants studied over a five year period.22 While the early prophylactic regimens reduce the incidence of early infection, the risk of late infection remains.23 Deficiencies in the function of immunoregulatory genes that activate the cellular and humoral immune responses can be the underlying cause of an increased risk of infection. As part of the immune system’s response to infection, activation of T cells through TNFSF4 costimulation has been shown to effectively clear pathogens.15,24 Genetic variation may influence the timing and strength of TNFSF4 signaling to effectively respond to infectious pathogens.
In this study, we carefully chose candidate SNPs found within a group of extensively studied costimulatory molecules that might associate with HSCT outcomes. We analyzed genetic data from a discovery (N=1157) and validation (N=1188) cohort using HLA-matched (at the HLA A, B, C, DRB1 and DQB1 loci) HSCT recipients and their respective donors and then searched for associations with important transplant outcomes.
Materials and Methods
Patient Population
A discovery cohort of 1157 and a validation cohort of 1188 recipient/donor pairs from unrelated HLA-A, B, C, DRB1, and DQB1 allele-matched transplantations facilitated by the National Marrow Donor Program (NMDP) were included in the study. A detailed description can be found under Supplementary Methods. Patient data was acquired from the Center of International Blood and Marrow Transplant Research (CIBMTR), a research affiliation between the Medical College of Wisconsin, and the NMDP. Observational studies conducted by the CIBMTR are performed in compliance with the Privacy Rule under the Health Insurance Portability and Accountability Act of 1966, as a Public Health Authority and in compliance with all applicable federal regulations pertaining to the protection of human research participants and the Declaration of Helsinki as determined by continuous review of the Institutional Review Board of the NMDP.
Definition of outcome
The primary endpoints analyzed in the study were overall survival (OS), disease free survival (DFS), treatment related mortality (TRM), relapse, acute graft versus host disease (aGVHD) grades II-IV and III-IV occurring within the first 100 days post-transplant, and chronic graft versus host disease (cGVHD). Our analysis of overall survival treated death from any cause as the event and surviving patients were censored at the date of last contact. For analysis of disease free survival (DFS), failures were relapse or death from any cause with patients who were alive and in complete remission censored at time of last follow-up. TRM was defined as death during a continuous complete remission. Relapse was defined as clinical or hematologic relapse of primary disease with death without evidence of disease as a competing risk. For CML patients our definition of relapse included cytogenetic, molecular and hematologic relapse as an event. Assessment of aGVHD Grades II-IV and III-IV were defined using the Glucksberg scale and extensive cGVHD was defined according to the Seattle criteria.25,26
Genotyping
We genotyped 9 SNPs located in 5 immunoregulatory genes: CD274, CD40, CD154, CD28 and TNFSF4. (Table S1) These SNPs were selected based on strong linkage disequilibrium to tagged SNPs within their gene and power calculations suggested in our population a high likelihood of discovering an association. For the discovery set, genomic DNA was isolated from cryopreserved leukocytes from donors and recipients blood samples provided by the NMDP Research Sample Repository following the manufacturer’s protocol (Promega). Isolated DNA was quantitated (Nanodrop; Thermo Scientific) and whole genome amplified with Phi29 DNA polymerase. For the validation set, genomic DNA was isolated from frozen blood samples using the QIAmp 96 DNA Blood Kit and the manufacturer’s protocol (Qiagen, Valencia, CA). Genotyping was then performed using a Taqman SNP genotyping assay (Applied Biosystems) and automated genotype calling software. Samples in the discovery (N=213) and validation (N=12) cohorts where a SNP genotype was not obtained due to DNA degradation or minimal isolated genomic DNA were excluded from further analysis.
SNP selection and identification of tag SNPs
SNPs were investigated using the Hapmap Genome Browser Phase 1&2- full dataset (International Hapmap Project, http://www.hapmap.org/). Gene SNP data from the Caucasian population was imported into Haploview (http://www.broad.mit.edu/mpg/haploview/) using the Hapmap format function. The uploaded SNP data was further modeled by setting the following Haploview parameters: Hardy-Weinberg p-value cutoff of 0.001; a minimum genotype percent of 80; a maximum number Mendel error of 1; and a minimum minor allele frequency (MAF) of 0.05. The MAF cutoff of 5% was selected for compatibility with HapMap which includes only those SNPs with allele frequencies greater than 0.05. The tagger function in Haploview was performed using pair-wise tagging only with an r2 equal to 0.8. Identified tag SNPs and the corresponding predicted common SNPs were compiled.
Statistical analysis
Discovery Set
Univariate probabilities of DFS and OS were calculated using the Kaplan-Meier method. The log-rank test was used for comparing survival curves. Probabilities of TRM, relapse, aGVHD, and cGVHD were calculated using cumulative incidence estimates. The cumulative incidence calculated for aGVHD and cGVHD treated death as a competing risk.27 Relapse was treated as a competing risk for TRM and vice versa.
The multivariate analysis of OS, DFS, TRM, relapse, acute and chronic GVHD were performed using the Cox proportional hazards regression models with a multivariate analysis performed to identify clinical variables that were associated with particular outcomes. Clinical variables considered in the models include disease, disease status, conditioning regimen, CMV match, Karnofsky score, graft type, GVHD prophylaxis, year of transplant, recipient age, donor age, and sex. For each outcome, all variables were examined for proportional hazards. Factors violating the proportional hazards assumption were adjusted by stratification. The stepwise model building approach was then used in developing models for all the outcomes. OS was adjusted for disease, disease stage, conditioning regimen, patient age, and GVHD prophylaxis and stratified by graft type and Karnofsky score. DFS was adjusted for disease, disease stage, CMV match, patient age, and conditioning regimen and stratified by disease type and Karnofsky score. Relapse was adjusted for disease, disease stage, conditioning regimen and donor sex. TRM was adjusted for disease stage, conditioning regimen and patient age and stratified by disease, graft type and Karnofsky score. Acute GVHD II-IV was adjusted for disease, disease stage, graft type and GVHD prophylaxis. Acute GVHD III-IV was adjusted for disease, disease stage, conditioning regimen and year of transplant. Chronic GVHD was adjusted for disease and patient age and stratified by GVHD prophylaxis.
Potential interactions between the genetic variants and clinical covariates were tested and were not present. Due to the possibility of Type I error occurring from multiple comparisons, the genetic associations were referred to as statistically significant when a P-value was ≤ 0.01. SAS software version 9.2 (SAS Institute) was used in all the analyses.
We employed a priori power calculations to determine the power of our cohort (n=1157 donor-recipient pairs) to detect significant associations between each gene SNP and HSCT outcomes. Setting the odds ratio (OR) to OR=1.5 for each gene SNP with minor allelic frequency (MAF) ≥ 5 percent, assuming a type 1 error level of 0.05, calculations were performed using the Quanto software (http://hydra.usc.edu/GxE/). Using our cohort we calculated the minimum number of individuals required to attain 80% power in our study to identify significant associations between HSCT outcomes and nine costimulatory gene SNPs.
For additional statistical analyses, the association between TNFSF4 SNP rs10912564 genotypes and causes of death were performed using a chi-squared test and TNFSF4 in vitro data were analyzed by Student’s t-test. Results were considered statistically significant when the P-value was ≤ 0.05.
Validation Set
To validate the SNP rs10912564 observations in the discovery cohort, we assembled an independent cohort of 1200 unrelated donor-recipient pairs using the same covariates from the discovery cohort.
DPI ELISA
DNA protein interaction enzyme linked immunosorbent assay (DPI ELISA) has been described previously.28 Briefly, we created 5’ biotin linked 30-mer double stranded DNA oligomer sequences corresponding to the rs10912564 region of TNFSF4 gene with rs10912564C/T located in the center position in bold (5’-TAGCAGTATGTTAACGGAAGCATGTTCATG-3’) and (5’-TAGCAGTATGTTAATGGAAGCATGTTCATG-3’) (Integrated DNA Technologies). 96 well plates were coated with streptavidin (15520, Pierce Biotechnologies) and incubated with 2 pmol DNA oligo probes for 1 hour at 37°C, blocked with 5% milk for 30 minutes and incubated with 5µg of Jurkat cell nuclear extract (36110, Active Motif). After 1 hour c-myb was detected by primary c-myb antibody (1:500, D-7, sc-74512) and anti-mouse horseradish peroxidase linked secondary (1:1500, sc-2031) antibody (Santa Cruz Biotechnologies). After each step, the wells were washed 3 times with PBST (phosphate buffer saline tween-20). An o-Phenylenedianime dihydrochloride (OPD) substrate (P9187, Sigma) produced a peroxidase reaction that was quantitated by measuring absorbance at 450 nm on an ELISA-reader.
Flow Cytometry
Peripheral blood mononuclear cells (PBMC) were available from the NMDP Research Repository for in vitro studies. PBMC from twelve HSCT donors were tested with four each from the CC, CT and TT rs10912564 genotypes. Cells were plated in a 96 well plate at 0.25×106 and were stimulated with 10 µg/mL F(ab’)2 anti-human IgM Fc5µ (Jackson Immunoresearch) and 5 µg/mL CD40 (S2C6) (Mabtech) monoclonal antibody for 72 hours shown to upregulate TNFSF4 and c-myb in B cells. 29,30 Cells were stained for 30 minutes on ice with CD19-FITC (HIB19), TNFSF4-PE (11C3.1) (Biolegend) and 7AAD (BD Biosciences) and run on a FACScan flow cytometer. A PE mouse lgG1 kappa MOPC-21 isotype control (Biolegend) was used to estimate nonspecific binding of the TNFSF4-PE antibody. Dead cells were excluded by 7AAD staining and a forward and side scatter lymphocyte gate was applied. The percentage of TNFSF+CD19+ high cells was analyzed using FloJo version 10 (Tree Star) and Prism version 4.0a (GraphPad Software, Inc.).
Results
Genotyping of costimulatory molecule genetic variants
Costimulatory molecule SNPs were genotyped using automated technology in a discovery (n=1370) and validation (n=1200) cohort totaling 2570 recipients and their respective donors. Genotyping was successful for 84.4% (1157) and 99% (1188) in our discovery and validation cohorts respectively. The allele frequencies observed in our predominately Caucasian transplant population were comparable to those obtained in the HAPMAP database phase II + III on NCBI B36 assembly, dbSNP b126.31 We found each genetic variant to be in Hardy-Weinberg equilibrium, confirming no evolutionary selection and validating the accuracy of our genotyping (Table S1). Focusing on the rs10912564 TNFSF4 genotypes, the characteristics (age at transplant, Karnofsky score, sex, disease, stage, etc) of C/C recipients shown in (Table 1) were not significantly different compared with the general population tested by χ2.
Table 1.
Characteristics of patients receiving a first myeloablative transplant for AML, ALL, CML or MDS, analyzed by rs10912564 SNP donor typing in the discovery cohort*
| TT + CT |
CC |
P-value |
|
|---|---|---|---|
| Patient characteristic |
N (%) | N (%) | |
|
Total number of patients |
606 | 551 | |
| Number of centers | 97 | 96 | |
| Age, median (range), years | 38 (<1–64) | 36 (1–65) | .93 |
|
Age at transplant, years |
.68 | ||
| 0–9 | 47 (8) | 38 (7) | |
| 10–19 | 62 (10) | 59 (11) | |
| 20–29 | 94 (16) | 96 (17) | |
| 30–39 | 165 (27) | 135 (25) | |
| 40–49 | 161 (27) | 140 (25) | |
| 50 and older | 77 (13) | 83 (15) | |
| Male sex | 352 (58) | 317 (58) | .85 |
|
Karnofsky prior to transplant > 90 |
442 (73) | 393 (71) | .22 |
|
Disease at transplant |
.74 | ||
| AML | 160 (26) | 131 (24) | |
| ALL | 114 (19) | 111 (20) | |
| CML | 237 (39) | 224 (41) | |
| MDS | 95 (16) | 85 (15) | |
|
Disease status at transplant |
.62 | ||
| Early | 285 (47) | 244 (44) | |
| Intermediate | 137 (23) | 142 (26) | |
| Advanced | 154 (25) | 136 (25) | |
| Other MDS | 30 (5) | 29 (5) | |
| Graft type | .44 | ||
| Bone marrow | 551 (91) | 508 (92) | |
| Peripheral blood | 55 (9) | 43 (8) | |
|
Conditioning regimen |
.75 | ||
| Cy/TBI +/− Other | 464 (77) | 415 (75) | |
| TBI +/− Other | 25 (4) | 20 (4) | |
| Bu/Cy +/− Other | 98 (16) | 103 (19) | |
| Busulfan + Melphalan +/− Other |
12 (2) | 8 (1) | |
| Other | 7 (1) | 5 (1) | |
| GVHD prophylaxis | .80 | ||
| Cyclosporine + MTX ± other |
354 (58) | 334 (61) | |
| Cyclosporine ± other (No MTX) |
18 (3) | 21 (4) | |
| Tacrolimus ± other | 124 (20) | 107 (19) | |
| MTX ± other (No Cyclosporine) |
7 (1) | 5 (1) | |
| Ex vivo T-cell depletion |
100 (17) | 83 (15) | |
| Other | 3 (<1) | 1 (<1) | |
|
Donor/recipient sex match |
.98 | ||
| Male/Male | 244 (40) | 223 (40) | |
| Male/Female | 142 (23) | 128 (23) | |
| Female/Male | 108 (18) | 94 (17) | |
| Female/Female | 112 (18) | 106 (19) | |
|
Donor/recipient CMV match |
.91 | ||
| Negative/Negative | 215 (35) | 205 (37) | |
| Negative/Positive | 166 (27) | 151 (27) | |
| Positive/Negative | 105 (17) | 93 (17) | |
| Positive/Positive | 103 (17) | 84 (15) | |
| Unknown | 17 (3) | 18 (3) | |
|
Donor age, median (range), years |
37 (18–60) | 35 (18–60) | .20 |
| Donor age, years | .55 | ||
| 18–19 | 6 (1) | 4 (1) | |
| 20–29 | 150 (25) | 150 (27) | |
| 30–39 | 226 (37) | 215 (39) | |
| 40–49 | 183 (30) | 143 (26) | |
| 50 and older | 41 (7) | 39 (7) | |
| Year of transplant | .15 | ||
| 1990 | 11 (2) | 17 (3) | |
| 1991 | 16 (3) | 21 (4) | |
| 1992 | 35 (6) | 21 (4) | |
| 1993 | 30 (5) | 26 (5) | |
| 1994 | 56 (9) | 37 (7) | |
| 1995 | 47 (8) | 37 (7) | |
| 1996 | 43 (7) | 36 (7) | |
| 1997 | 48 (8) | 46 (8) | |
| 1998 | 70 (12) | 54 (10) | |
| 1999 | 46 (8) | 69 (13) | |
| 2000 | 87 (14) | 83 (15) | |
| 2001 | 83 (14) | 69 (13) | |
| 2002 | 34 (6) | 35 (6) | |
|
Median follow-up of survivors, (range) months |
95 (3–194) | 96 (22–196) | .12† |
Data has been CAP-modeled.
Log-rank P-value.
ALL indicates, acute lymphoblastic leukemia; AML, acute myeloid leukemia; BU, busulfan; CLL, chronic lymphocytic leukemia; CMV, cytomegalovirus; Cy, cyclophosphamide; MDS, myelodysplastic syndromes; MTX, methotrexate; and TBI, total body irradiation.
Outcome analysis by donor and recipient costimulatory molecule genotypes
Initially we examined the association between costimulatory molecule SNPs and transplant outcomes in our discovery cohort. We found a significant association with increased OS (HR, 0.823; 95% CI, 0.718–0.953; P =.0085; Table 2; Figure 1), in donors carrying the TNFSF4 CC genotype compared to TT or CT. Interestingly, we found a significant decrease in TRM (HR, 0.778; 95% CI, 0.650– 0.931; P = .006; Table 2; Figure 1) in CC rs10912564 genotyped donors. When TRM was analyzed using T cell depletion status we observed similar hazard ratios suggesting T cell depletion status did not affect TRM (Table S2). There was no association between rs10912564 with other HSCT outcomes such as relapse, aGVHD or cGVHD (Table 2; Figure 1). SNPs in genes CD274 and CD40 of recipients were associated with HSCT outcomes however; require additional validation testing (Table S3).
Table 2.
Discovery and Validation cohort multivariate analysis of donor rs10912564 genotypes TT + CT versus CC.
| Outcome | rs10912564 genotype |
N | HR | 95% confidence interval |
P |
|---|---|---|---|---|---|
|
Overall survival |
|||||
| Discovery | TT + CT CC |
606 551 |
0.823 | (0.718–0.953) | 0.0085 |
| Validation | TT + CT CC |
614 571 |
1.036 | (0.893–1.202) | 0.6405 |
|
Disease-free survival |
|||||
| Discovery | TT + CT CC |
606 551 |
0.855 | (0.743–0.984) | 0.0291 |
| Validation | TT + CT CC |
604 561 |
1.059 | (0.915–1.226) | 0.4408 |
|
Treatment- related mortality |
|||||
| Discovery | TT + CT CC |
606 551 |
0.778 | (0.65–0.931) | 0.006 |
| Validation | TT + CT CC |
606 561 |
1.013 | (0.823–1.247) | 0.9039 |
| Relapse | |||||
| Discovery | TT + CT CC |
606 551 |
0.979 | (0.777–1.233) | 0.857 |
| Validation | TT + CT CC |
604 561 |
1.083 | (0.883–1.329) | 0.4448 |
|
Acute GVHD grade II-IV |
|||||
| Discovery | TT + CT CC |
606 551 |
0.903 | (0.769–1.06) | 0.2117 |
| Validation | TT + CT CC |
614 572 |
0.995 | (0.841–1.177) | 0.951 |
|
Acute GVHD grade III-IV |
|||||
| Discovery | TT + CT CC |
606 551 |
1.062 | (0.838–1.346) | 0.618 |
| Validation | TT + CT CC |
606 564 |
0.948 | (0.731–1.230) | 0.6888 |
| Chronic GVHD | |||||
| Discovery | TT + CT CC |
606 551 |
0.931 | (0.777–1.114) | 0.4339 |
| Validation | TT + CT CC |
600 557 |
0.986 | (0.838–1.160) | 0.8648 |
Comparison for all categories is (TT + CT) vs. CC. GVHD indicates graft-versus-host disease; and HR, hazard ratio. Covariates used included disease, disease status, conditioning regimen, Cytomegalovirus match, Karnofsky score, graft type, GVHD prophylaxis, year of transplant, recipient age, donor age, and sex.
Figure 1. Donor TNFSF4 rs10912564 C/C genotype versus C/T and T/T in a discovery cohort.
(A) Kaplan-Meier curves for (A) disease free survival (DFS), (B) Patient overall survival (OS), (C) Cumulative incidence of treatment related mortality (TRM) and (D) Cumulative incidence of relapse in the 10 years after transplantation. (E) Cumulative incidence curves for acute Graft-vs-Host Disease (aGVHD) grade II to IV (E) and III to IV (F) in the first 100 days after transplantation.
To determine if SNP rs10912564 has any effect on mortality in our patient population, we analyzed causes of death (COD) comparing patients transplanted with rs10912564 CC or TT/TC genotyped donors (Table S4). We found a significant increase in mortality due to infection in the TT/TC group compared to CC in our discovery cohort (P = .014). This association was present in patients with no GVHD and in contrast was absent in the GVHD cohort. (Table S5). A cause of death analysis is difficult to define a specific variable and these results require further investigation into the rate of infection. Although a larger and well designed study is needed to further support these data, TNFSF4 has been shown to be critically required to maintain T cell responses during persistent viral infections.32
To validate the associations observed with TNFSF4 SNP rs10912564, we identify a much more recent cohort available to us through the NMDP. We found no association between SNP rs10912564 and transplant outcomes or cause of death in this validation cohort (Table 2 and Table S6). Not surprisingly, we noted that a number of contributing factors, which were significantly different between the cohorts including age, disease type, graft type, GVHD prophylaxis, donor age and year of transplant (Table 3). These factors could potentially impact the clinical relevance of SNP rs10912564 TNFSF4 and transplant outcomes. We further performed a subset analysis of bone marrow graft type comparing OS, DFS and TRM outcomes in the validation cohort. There was no association, whether the model was adjusted for covariates identified from the discovery (all P >.44) or validation (all P >.46) cohorts.
Table 3.
Characteristics of patients receiving myeloablative transplant for AML, ALL, CML or MDS reported to the NMDP for the discovery and validation cohorts
| Discovery cohort |
Validation cohort |
P |
|
|---|---|---|---|
| Variable | N (%) | N (%) | |
| Number of patients | 1301 | 1200 | |
| Number of centers | 119 | 125 | |
| Age, median (range), years | 36 (<1–65) | 38 (<1 – 70) | .003 |
| Age at transplant, years | < 0.0001 | ||
| <1–9 | 99 (8) | 90 (8) | |
| 10–19 | 133 (10) | 152 (13) | |
| 20–29 | 206 (16) | 212 (18) | |
| 30–39 | 337 (26) | 186 (16) | |
| 40–49 | 344 (26) | 263 (22) | |
| 50 and older | 182 (14) | 297 (25) | |
| Recipient race | |||
| Caucasian | N/A | 1059 (90) | |
| African-American | N/A | 31 (3) | |
| Asian/Pacific Islander | N/A | 12 (1) | |
| Hispanic | N/A | 66 (5) | |
| Native American | N/A | 3 (<1) | |
| Other/Missing/Unknown/TBD | N/A | 29 | |
| Male sex | 747 (57) | 673 (56) | .5 |
| Karnofsky score prior to transplant | .51 | ||
| < 90 | 321 (25) | 280 (23) | |
| ≥ 90 | 936 (72) | 767 (64) | |
| Missing/Unknown | 44 | 153 | |
| Disease | <0.0001 | ||
| AML | 326 (25) | 571 (48) | |
| ALL | 251 (19) | 350 (29) | |
| CML | 526 (40) | 175 (15) | |
| MDS | 198 (15) | 104 (9) | |
| Disease stage | |||
| Early | 589 (45) | 500 (42) | |
| Intermediate | 325 (25) | 377 (31) | |
| Advanced/Late | 323 (25) | 323 (27) | |
| Other | 64 (5) | 0 | |
| Graft Type | <0.0001 | ||
| Marrow | 1195 (92) | 490 (41) | |
| PBSC | 106 (8) | 710 (59) | |
| Donor SNP rs10912564 | .88 | ||
| TT | 111 (10) | 119 (10) | |
| CT | 495 (43) | 497 (42) | |
| CC | 551 (48) | 572 (48) | |
| Missing/Unknown | 144 | 12 | |
| Donor/Recipient sex match | .53 | ||
| Male / Male | 527 (41) | 475 (40) | |
| Male / Female | 309 (24) | 315 (26) | |
| Female / Male | 220 (17) | 198 (17) | |
| Female / Female | 245 (19) | 212 (18) | |
| Donor/Recipient CMV match | <0.0001 | ||
| Donor − / Recipient − | 477 (37) | 387 (32) | |
| Donor − / Recipient + | 357 (27) | 440 (37) | |
| Donor + / Recipient − | 215 (17) | 140 (12) | |
| Donor + / Recipient + | 215 (17) | 213 (18) | |
| Unknown | 37 (3) | 20 (2) | |
| GVHD prophylaxis | <0.0001 | ||
| FK506 + (MTX or MMF or Steroids) ± Other |
281 (22) | 681 (57) | |
| FK506 ± Other | 3 (<1) | 72 (6) | |
| CSA + MTX ± Other | 760 (58) | 288 (24) | |
| CSA ± Other | 38 (3) | 53 (4) | |
| MTX ± Other | 4 (<1) | 0 | |
| Ex vivo T-Cell Depletion | 212 (16) | 83 (7) | |
| Other | 3 (<1) | 23 (2) | |
| Donor age in years, Median (range) | 36 (18–60) | 33 (18 – 60) | <0.0001 |
| Donor age in decades | <0.0001 | ||
| 18–19 | 12 (1) | 18 (2) | |
| 20–29 | 336 (26) | 410 (34) | |
| 30–39 | 505 (39) | 449 (37) | |
| 40–49 | 354 (27) | 265 (22) | |
| 50 and older | 94 (7) | 58 (5) | |
| Donor race | |||
| Caucasian | N/A | 1033 (89) | |
| African American | N/A | 30 (3) | |
| Asian/Pacific Islander | N/A | 11 (1) | |
| Hispanic | N/A | 17 (1) | |
| Native American | N/A | 13 (1) | |
| Other/ Declined/Unknown | N/A | 62 (5) | |
| Missing/TBD | N/A | 34 | |
| Year of transplant | <0.0001 | ||
| 1990 | 30 (2) | 0 | |
| 1991 | 46 (4) | 0 | |
| 1992 | 58 (4) | 0 | |
| 1993 | 61 (5) | 0 | |
| 1994 | 105 (8) | 0 | |
| 1995 | 98 (8) | 0 | |
| 1996 | 86 (7) | 0 | |
| 1997 | 118 (9) | 0 | |
| 1998 | 144 (11) | 0 | |
| 1999 | 137 (11) | 0 | |
| 2000 | 181 (14) | 0 | |
| 2001 | 161 (12) | 0 | |
| 2002 | 76 (6) | 0 | |
| 2003 | 0 | 165 (14) | |
| 2004 | 0 | 248 (21) | |
| 2005 | 0 | 324 (27) | |
| 2006 | 0 | 354 (30) | |
| 2007 | 0 | 109 (9) | |
| Median follow-up of survivors, months | 96 (3–196) | 71 (11 – 109) | <0.0001* |
Log-rank p-value.
Analysis of linkage of genetic variants in TNFSF4
In the TNFSF4 gene encoded on chromosome 1, we identified rs10912564C/T as a potential SNP marker based on its minor genotypic frequency TT > 0.05 within the Caucasian population and ability to tag in linkage five other SNPs (rs7525284, rs4081545, rs3861950, rs11811856, and rs7514229) each located in the first intron of TNFSF4 except rs7514229 which is in the 3’ untranslated region (UTR) (Figure 2). Due to the strong linkage disequilibrium, the haplotype created by the six SNPs had a minor allelic frequency (29.4%) in Caucasians from the CEU HapMap population that was similar to the frequency of the rs10912564 T allele (31.8%) in our patient population. Each tagged TNFSF4 SNP was scored for functional relevance using the F-SNP bioinformatics algorithm which cross-references 16 bioinformatics tools and databases to generate a SNP score on a 0–1 scale where high scoring SNPs are known to be disease associated.33,34 The algorithm utilized TFSearch version 1.3 to score potential transcription factor binding sites in the C and T rs10912564 SNP variant sequence with a threshold of 85. A score of 90.9 was observed between the C variant and myeloblastosis viral oncogene homolog (v-myb), which was not present in the T variant. The F-SNP readout indicated v-myb was the only transcription factor that differed between the two SNP rs10912564 variants. F-SNP analysis of the SNPs tagged with rs10912564 showed rs4081545 as a potential binding site for nuclear factor-1 (NF-1) and rs7514229 as a site for caudal type homeobox 1 (Cdx-1) with scores above threshold (Figure 2).
Figure 2. TNFSF4 gene organization as depicted in the NCBI database and analysis of SNPs in linkage with rs10912564.
The TNFSF4 gene is on chromosome 1q25 and is a 3-exon, 2-intron gene. Filled boxes denote exons, unfilled boxes indicate untranslated regions and solid lines between boxes represent introns in the TNFSF4 gene diagram. The relative positions of the TNFSF4 variants tagged with rs10912564 are indicated along with their percent of linkage disequilibrium calculated in Haploview. A haplotype frequency is noted for the linked major alleles compared to minor alleles. The assessment of each SNPs functional relevance was calculated using F-SNP bioinfomatics algorithm. Potential transcription factor binding sites altered by the SNP polymorphism are listed.
Functional relevance of TNFSF4 SNP rs10912564
To determine whether rs10912564 had any effect on the function of TNFSF4, we initially utilized the SNP bioinformatics database F-SNP to search for potential functional changes in protein coding, splicing regulation, transcriptional regulation, transcription regulation or post translation involving SNP rs10912564. We found rs10912564 was located within a potential Myb binding site. To further explore the possibility that the rs10912564 T/C variant could augment the binding of transcription factor c-myb we created a DPI ELISA assay to assess the ability of c-myb to bind a biotinylated 30mer sequence of TNFSF4 containing either the T or C rs10912564 variant. We utilized Jurkat cells shown to express c-myb in nuclear extracts as our source of c-myb protein 35. Upon comparison, we found c-myb bound significantly stronger to the C biotinylated probe compared to the T variant (Figure 3A, P = .0024). In order to check the specificity of the assay we performed a competition experiment. As shown in Figure 3B, adding excess amount of non-biotin labeled probe was able abrogate c-myb binding to plate bound rs10912564C probe. To functionally determine if these variants effect cellular responses we collected PBMC from 4 donors of each rs10912564 genotype and stimulated them with IgM and anti-CD40 antibodies for 72 hours and assessed their ability to upregulate TNFSF4 on B cells. Stimulation lead to an increase in the percentage of TNFSF4+CD19+ B cells in donors carrying the CC genotype compared to CT and TT suggesting the rs10912564C allele may augment the expression level of TNFSF4 (Figure 4, P = .049). These data highlight the potential functional capacity of SNP rs10912564 to regulate transcription factor binding to TNFSF4 and the expression level on the cell surface of B cells.
Figure 3. Specific binding of c-myb to a 30-oligomer sequence of TNFSF4 containing the rs10912564 polymorphism.
(A) Binding of c-myb to a 30-mer oligonucleotide probe containing either the rs10912564 C or T variant in the center. Binding was measured as a function of absorbance at 450 nm following a peroxidase reaction. Data represent 3 independent experiments. (B) The specific binding of c-myb to the rs10912564C probe was shown by a competition experiment with non-biotinylated dsDNA. Different concentrations of probe (100, 1000pmol) were added to Jurkat nuclear extract and incubated on an ELISA-plate coated with 2pmol of biotinylated rs10912564C probe and absorbance read at 450 nm following a peroxidase reaction. Data represent 2 independent experiments. B, blank; Bio, Biotinylated; and c-myb, myeloblastosis viral oncogene homolog
Figure 4. TNFSF4 surface expression on CD19+ B cells from HSCT donor PBMC.
PBMC (n=12) from each TNFSF4 rs10912564 genotype (n=4); were stimulated with 10 µg/mL IgM and 5 µg/mL anti-CD40 antibodies for 72 hours. (A) Overlayed histograms were normalized and depicted as the % of max for the CC (i), TC (ii), and TT (iii) genotypes. The black histograms indicate TNFSF4 staining of stimulated B cells and the grey histograms indicate TNFSF4 staining of unstimulated B cells. A negative baseline cutoff was created based on isotype staining demarcated where the negative and positive histogram markers overlap. (B) The percentage of TNFSF4 positive B cells was calculated in the presence and absence of stimulation. The average difference in the percentage of TNFSF4 positive B cells with and without stimulation for each genotype was plotted with standard error of mean.
Discussion
Efforts to identify risk factors such as HLA mismatch have had a significant impact on improving the outcomes of HSCT.36,37 These studies allowed for better matching of recipients and donors. However, the rate of post HSCT complications remains high. A main focus has been to investigate the importance of genetic polymorphisms in immunoregulatory genes in determining HSCT outcomes post transplant. 5,38–40 Virtually all post HSCT complications are immune response driven therefore, we focused on the role of costimulatory molecules by examining the association between 9 SNPs in 5 costimulatory genes and HSCT outcomes in unrelated HLA matched donor-recipient pairs. These costimulatory molecules play a key role in determining the outcome of immune responses.41–43 Our dataset of 2570 donor-recipient pairs collected from two independent cohorts represents one of the largest SNP studies assembled focused on TNFSF4, which is expressed on B cells, vascular endothelial cells, natural killer cells and mast cells and provides clonal expansion of antigen-specific T cells, expansion of T regs and generation of T-cell memory.15,44
The TNFSF4 rs10912564CC genotype of donors was associated with a higher likelihood of overall survival with less treatment-related mortality in our discovery cohort. This association between rs10912564CC and less treatment related mortality may reflect the costimulatory capacity of the reconstituted immune system by the donor leukocytes 9. In support of our discovery findings, it was recently demonstrated in a cohort of patients that low responders to hepatitis B vaccination were more likely to have the rs10912564 T allele.45 The effect of TNFSF4 on infection clearance has been heavily supported in a number of studies. For instance, prolonged immunity has been observed against mycobacterium tuberculosis when vaccination is delivered in conjunction with a TNFSF4:Ig fusion protein generating a strong Th1 cytokine response.46,16,47 Furthermore, T cells lacking TNFRSF4 fail to accumulate in response to a chronic viral infection which is associated with decreased survival proteins Bcl-2 and Bcl-xL.32 In future studies, a correlation between infection rate with genetic variants in marrow grafts could identify individuals at higher risk for infection following HSCT and would allow physicians to create a more stringent plan for monitoring and treating this subset of patients in order to optimize clinical outcomes.
Since we evaluated a clinical association between rs10912564 genotypes and HSCT outcomes, we strived to develop a possible mechanism of action. Functional SNPs are known to mainly interact in gene promoter regions; however, non-coding intronic sequence may contain functional SNPs that can enhance the transcriptional level of genes leading to disease associations. 48,49 Regulatory elements located in the first intron have been identified as sites of transcriptional regulation.50 Examination of SNP rs10912564 using bioinfomatics tools helped us to pinpoint the c-myb binding site consensus sequence as a possible functionally relevant site in which the C nucleotide would be predicted to be a critical base required for c-myb binding.51 We speculate that rs10912564C creates a stable binding site for c-myb that could potentially augment the expression of TNFSF4 in comparison to the T allele. Many genes are upregulated through the binding of c-myb, which is induced following activation of T and B cells and is required for the differentiation and survival of the B cell lineage.52–54 The location of the rs10912564 polymorphism in the intron 1 region suggests it effects the creation of a transactivator enhancer or interacts with regulatory elements required for gene expression.
This study has strengths and limitations. First, this study represents one of the largest non-HLA genetic association studies using a well-defined discovery cohort of 1157 donor-recipient pairs. We meticulously chose SNPs in candidate immunoregulatory genes, which were known to have pivotal roles in various outcomes post transplantation. The main limitation in this study was the inability to replicate our association data in a more recent validation cohort where the significance of TNFSF SNP rs10912564 was negligible suggesting the absence of a true association or indicative of a small size effect. The later is an important aspect of gene association studies for complex diseases, as most individual disease-associated SNPs exhibit modest effects on relative risk. A typed SNP may serve as an imperfect surrogate for the true causal SNP suggesting a joint effect of multiple SNPs. Multiple comparisons in clinical outcome driven SNP studies might require a more stringent p value, however, our TRM p value (.006) met rigidity required to adjust for multiple comparisons. Furthermore, we tried to reduce the burden of multiple comparisons by having high statistical power with prior literature supported knowledge of the effect these candidate genes had on HSCT outcomes. If the ever-evolving field of clinical bone marrow transplantation obscured the effect of our SNP, it sheds light on a challenge for genetic association studies in this field and virtually for all other fields that therapies and standard of practice change over time. Furthermore, the actual contribution of a SNP to the biology of a gene and novel insights drawn from the data, even obscured, facilitates better understanding of the mechanisms by which the gene might affect disease.
The lack of validation could also be to due to the significant differences between the discovery and validation cohorts. The validation cohort included an increased number of older patients than the discovery cohort and this older patient population was associated with more acute leukemias. Furthermore, the newer validation cohort had access to a much younger donor population highlighting improvement in donor options via registry growth in recent years. Additionally, the validation group benefited from recent changes in HSCT technique in that graft types have transitioned to peripheral blood stem cell transplants. Lastly, the validation cohort utilized more FK506/Tacrolimus for GVHD prophylaxis, which has been associated with less aGVHD and better outcomes compared to the CSA based treatments utilized in the discovery cohort.55 Moreover, ex vivo T cell depletion was utilized in the discovery cohort and is associated with poor engraftment and higher relapse rates.56 Conversely, the validation cohort had little to no ex vivo T cell depletion. While, we made an effort to incorporate the differences between the discovery and validation cohorts in our analysis, we cannot completely rule out that they did not significantly impact the effect of SNP rs10912564 on HSCT outcomes.
In conclusion, while we found associations between TNFSF SNP rs10912564 and HSCT outcomes in our discovery cohort and were able to show the functional contribution of the SNP, our findings did not validate in a more recent cohort. We believe a larger prospective study is warranted to examine the influence of TNFSF4 genetic variants on post transplant outcomes as well as in depth functional studies on specific subsets of immune cells to determine the potential impact of TNFSF4 genetic variants regulating their function.
Supplementary Material
Keypoints.
Donors homozygous for the C variant of SNP rs10912564 were associated with better patient outcomes in the discovery cohort but not in the validation cohort.
The C variant of SNP rs10912564 creates a higher affinity transcription factor binding site for Myb compared to T.
Highlights.
Analysis of SNPs in costimulatory molecules effect HSCT outcomes.
2345 donor-recipient pairs from two cohorts were included in this study.
Potential improved patient outcomes with TNFSF4 rs10912564CC genotyped donors.
The C variant of SNP rs10912564 creates a binding site for Myb compared to T.
Acknowledgments
This work was partly supported by the Division of Intramural Research of the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD. The CIBMTR is supported by Public Health Service Grant/Cooperative Agreement U24-CA76518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI) and the National Institute of Allergy and Infectious Diseases (NIAID); a Grant/Cooperative Agreement 5U01HL069294 from NHLBI and NCI; a contract HHSH234200637015C with Health Resources and Services Administration (HRSA/DHHS); two Grants N00014-12-1-0142 and N00014-13-1-0039 from the Office of Naval Research; and grants from Allos, Inc.; Amgen, Inc.; Angioblast; Anonymous donation to the Medical College of Wisconsin; Ariad; Be the Match Foundation; Blue Cross and Blue Shield Association; Buchanan Family Foundation; Caridian BCT; Celgene Corporation; CellGenix, GmbH; Children’s Leukemia Research Association; Fresenius-Biotech North America, Inc.; Gamida Cell Teva Joint Venture Ltd.; Genentech, Inc.; Genzyme Corporation; GlaxoSmithKline; HistoGenetics, Inc.; Kiadis Pharma; The Leukemia & Lymphoma Society; The Medical College of Wisconsin; Merck & Co, Inc.; Millennium: The Takeda Oncology Co.; Milliman USA, Inc.; Miltenyi Biotec, Inc.; National Marrow Donor Program; Optum Healthcare Solutions, Inc.; Osiris Therapeutics, Inc.; Otsuka America Pharmaceutical, Inc.; RemedyMD; Sanofi; Seattle Genetics; Sigma-Tau Pharmaceuticals; Soligenix, Inc.; StemCyte, A Global Cord Blood Therapeutics Co.; Stemsoft Software, Inc.; Swedish Orphan Biovitrum; Tarix Pharmaceuticals; Teva Neuroscience, Inc.; THERAKOS, Inc.; and Wellpoint, Inc. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Authorship Contributions
Contribution: Research design P.T.J., S.E.C., E.M., P.K., D.H.M. and R.A. Samples were genotyped by S.M.R., S.L.A and S.F.P. Data generated for Tables and Figures was done by P.T.J., S.E.C., S.S. and T.W. Statistical data analysis was done by T.W., M.H. and S.S. All the authors contributed to the writing of the manuscript and analysis of the data.
Conflict-of-Interest Disclosure
The authors declare no competing financial interests.
References
- 1.Bhatia S, Francisco L, Carter A, et al. Late mortality after allogeneic hematopoietic cell transplantation and functional status of long-term survivors: report from the Bone Marrow Transplant Survivor Study. Blood. 2007;110(10):3784–3792. doi: 10.1182/blood-2007-03-082933. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Bieri S, Roosnek E, Ozsahin H, et al. Outcome and risk factors for late-onset complications 24 months beyond allogeneic hematopoietic stem cell transplantation. Eur J Haematol. 2011;87(2):138–147. doi: 10.1111/j.1600-0609.2011.01638.x. [DOI] [PubMed] [Google Scholar]
- 3.Hansen JA, Chien JW, Warren EH, Zhao LP, Martin PJ. Defining genetic risk for graft-versus-host disease and mortality following allogeneic hematopoietic stem cell transplantation. Curr Opin Hematol. 2010;17(6):483–492. doi: 10.1097/MOH.0b013e32833eb770. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Dickinson AM, Holler E. Polymorphisms of cytokine and innate immunity genes and GVHD. Best Pract Res Clin Haematol. 2008;21(2):149–164. doi: 10.1016/j.beha.2008.03.004. [DOI] [PubMed] [Google Scholar]
- 5.Conway SE, Abdi R. Immunoregulatory gene polymorphisms and graft-versus-host disease. Expert Rev Clin Immunol. 2009;5(5):523–534. doi: 10.1586/eci.09.44. [DOI] [PubMed] [Google Scholar]
- 6.Ting C, Alterovitz G, Merlob A, Abdi R. Genomic studies of GVHD-lessons learned thus far. Bone Marrow Transplant. 2013;48(1):4–9. doi: 10.1038/bmt.2012.9. [DOI] [PubMed] [Google Scholar]
- 7.Sayegh MH. Looking into the crystal ball: kidney transplantation in 2025. Nat Clin Pract Nephrol. 2009;5(3):117. doi: 10.1038/ncpneph1042. [DOI] [PubMed] [Google Scholar]
- 8.Linsley PS, Ledbetter JA. The role of the CD28 receptor during T cell responses to antigen. Ann Rev Immunol. 1993;11:191–212. doi: 10.1146/annurev.iy.11.040193.001203. [DOI] [PubMed] [Google Scholar]
- 9.Bluestone JA. New perspectives of CD28-B7-mediated T cell costimulation. Immunity. 1995;2:555–559. doi: 10.1016/1074-7613(95)90000-4. [DOI] [PubMed] [Google Scholar]
- 10.Rothstein DM, Sayegh MH. T-cell costimulatory pathways in allograft rejection and tolerance. Immunol Rev. 2003;196:85–108. doi: 10.1046/j.1600-065x.2003.00088.x. [DOI] [PubMed] [Google Scholar]
- 11.Boenisch O, Sayegh MH, Najafian N. Negative T-cell costimulatory pathways: their role in regulating alloimmune responses. Curr Opin Organ Transplant. 2008 Aug;13(4):373–378. doi: 10.1097/MOT.0b013e328306117f. [DOI] [PubMed] [Google Scholar]
- 12.Yu XZ, Martin PJ, Anasetti C. Role of CD28 in acute graft-versus-host disease. Blood. 1998;92(8):2963–2970. [PubMed] [Google Scholar]
- 13.Tsukada N, Akiba H, Kobata T, Aizawa Y, Yagita H, Okumura K. Blockade of CD134 (OX40)-CD134L interaction ameliorates lethal acute graft-versus-host disease in a murine model of allogeneic bone marrow transplantation. Blood. 2000;95(7):2434–2439. [PubMed] [Google Scholar]
- 14.Blazar BR, Carreno BM, Panoskaltsis-Mortari A, et al. Blockade of programmed death-1 engagement accelerates graft-versus-host disease lethality by an IFN-gamma-dependent mechanism. J Immunol. 2003;171(3):1272–1277. doi: 10.4049/jimmunol.171.3.1272. [DOI] [PubMed] [Google Scholar]
- 15.Hori T. Roles of OX40 in the pathogenesis and the control of diseases. Int J Hematol. 2006;83(1):17–22. doi: 10.1532/IJH97.05151. [DOI] [PubMed] [Google Scholar]
- 16.Wythe SE, Dodd JS, Openshaw PJ, Schwarze J. OX40 ligand and programmed cell death 1 ligand 2 expression on inflammatory dendritic cells regulates CD4 T cell cytokine production in the lung during viral disease. J Immunol. 2012;188(4):1647–1655. doi: 10.4049/jimmunol.1103001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Mousavi SF, Soroosh P, Takahashi T, et al. OX40 costimulatory signals potentiate the memory commitment of effector CD8+ T cells. J Immunol. 2008;181(9):5990–6001. doi: 10.4049/jimmunol.181.9.5990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ria M, Lagercrantz J, Samnegard A, Boquist S, Hamsten A, Eriksson P. A common polymorphism in the promoter region of the TNFSF4 gene is associated with lower allele-specific expression and risk of myocardial infarction. PLoS One. 2011;6(3):e17652. doi: 10.1371/journal.pone.0017652. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Cunninghame Graham DS, Graham RR, Manku H, et al. Polymorphism at the TNF superfamily gene TNFSF4 confers susceptibility to systemic lupus erythematosus. Nat Genet. 2008;40(1):83–89. doi: 10.1038/ng.2007.47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Bossini-Castillo L, Broen JC, Simeon CP, et al. A replication study confirms the association of TNFSF4 (OX40L) polymorphisms with systemic sclerosis in a large European cohort. Ann Rheum Dis. 2011;70(4):638–641. doi: 10.1136/ard.2010.141838. [DOI] [PubMed] [Google Scholar]
- 21.Parody R, Martino R, Rovira M, et al. Severe infections after unrelated donor allogeneic hematopoietic stem cell transplantation in adults: comparison of cord blood transplantation with peripheral blood and bone marrow transplantation. Biol Blood Marrow Transplant. 2006;12(7):734–748. doi: 10.1016/j.bbmt.2006.03.007. [DOI] [PubMed] [Google Scholar]
- 22.Pasquini M, Wang Z. Current uses and outcomes of hematopoietic stem cell transplantation:CIBMTR summary slides. 2010 [Google Scholar]
- 23.Copelan EA. Hematopoietic Stem-Cell Transplantation. The New England Journal of Medicine. 2006;354(17):1813–1826. doi: 10.1056/NEJMra052638. [DOI] [PubMed] [Google Scholar]
- 24.Akiba H, Miyahira Y, Atsuta M, et al. Critical contribution of OX40 ligand to T helper cell type 2 differentiation in experimental leishmaniasis. J Exp Med. 2000;191(2):375–380. doi: 10.1084/jem.191.2.375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Glucksberg H, Storb R, Fefer A, et al. Clinical manifestations of graft-versus-host disease in human recipients of marrow from HL-A-matched sibling donors. Transplantation. 1974;18(4):295–304. doi: 10.1097/00007890-197410000-00001. [DOI] [PubMed] [Google Scholar]
- 26.Shulman HM, Sullivan KM, Weiden PL, et al. Chronic graft-versus-host syndrome in man. A long-term clinicopathologic study of 20 Seattle patients. Am J Med. 1980;69(2):204–217. doi: 10.1016/0002-9343(80)90380-0. [DOI] [PubMed] [Google Scholar]
- 27.Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med. 1999;18(6):695–706. doi: 10.1002/(sici)1097-0258(19990330)18:6<695::aid-sim60>3.0.co;2-o. [DOI] [PubMed] [Google Scholar]
- 28.Brand LH, Kirchler T, Hummel S, Chaban C, Wanke D. DPI-ELISA: a fast and versatile method to specify the binding of plant transcription factors to DNA in vitro. Plant Methods. 2010;6:25. doi: 10.1186/1746-4811-6-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Xiao C, Calado DP, Galler G, et al. MiR-150 controls B cell differentiation by targeting the transcription factor c-Myb. Cell. 2007;131(1):146–159. doi: 10.1016/j.cell.2007.07.021. [DOI] [PubMed] [Google Scholar]
- 30.Takasawa N, Ishii N, Higashimura N, et al. Expression of gp34 (OX40 ligand) and OX40 on human T cell clones. Jpn J Cancer Res. 2001;92(4):377–382. doi: 10.1111/j.1349-7006.2001.tb01105.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Frazer KA, Ballinger DG, Cox DR, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449(7164):851–861. doi: 10.1038/nature06258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Boettler T, Moeckel F, Cheng Y, et al. OX40 facilitates control of a persistent virus infection. PLoS Pathog. 2012;8(9):e1002913. doi: 10.1371/journal.ppat.1002913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Lee PH, Shatkay H. F-SNP: computationally predicted functional SNPs for disease association studies. Nucleic Acids Res. 2008;36:D820–D824. doi: 10.1093/nar/gkm904. (Database issue) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Heinemeyer T, Wingender E, Reuter I, et al. Databases on transcriptional regulation: TRANSFAC, TRRD and COMPEL. Nucleic Acids Res. 1998;26(1):362–367. doi: 10.1093/nar/26.1.362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Quintana AM, Zhou YE, Pena JJ, O’Rourke JP, Ness SA. Dramatic repositioning of c-Myb to different promoters during the cell cycle observed by combining cell sorting with chromatin immunoprecipitation. PLoS One. 2011;6(2):e17362. doi: 10.1371/journal.pone.0017362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Petersdorf EW. Genetics of graft-versus-host disease: The major histocompatibility complex. Blood Rev. 2013;27(1):1–12. doi: 10.1016/j.blre.2012.10.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Woolfrey A, Klein JP, Haagenson M, et al. HLA-C antigen mismatch is associated with worse outcome in unrelated donor peripheral blood stem cell transplantation. Biol Blood Marrow Transplant. 2011;17(6):885–892. doi: 10.1016/j.bbmt.2010.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Dickinson AM, Harrold JL, Cullup H. Haematopoietic stem cell transplantation: can our genes predict clinical outcome? Expert Rev Mol Med. 2007;9(29):1–19. doi: 10.1017/S1462399407000488. [DOI] [PubMed] [Google Scholar]
- 39.Ambruzova Z, Mrazek F, Raida L, et al. Association of IL6 and CCL2 gene polymorphisms with the outcome of allogeneic haematopoietic stem cell transplantation. Bone Marrow Transplant. 2009;44(4):227–235. doi: 10.1038/bmt.2009.16. [DOI] [PubMed] [Google Scholar]
- 40.Perez-Garcia A, De la Camara R, Roman-Gomez J, et al. CTLA-4 polymorphisms and clinical outcome after allogeneic stem cell transplantation from HLA-identical sibling donors. Blood. 2007;110(1):461–467. doi: 10.1182/blood-2007-01-069781. [DOI] [PubMed] [Google Scholar]
- 41.Ambruzova Z, Mrazek F, Raida L, et al. Association of IL-6 gene polymorphism with the outcome of allogeneic haematopoietic stem cell transplantation in Czech patients. Int J Immunogenet. 2008;35(4–5):401–403. doi: 10.1111/j.1744-313X.2008.00798.x. [DOI] [PubMed] [Google Scholar]
- 42.Mariam Azarian ea. Donor CTLA-4 +49 A/G*GG genotype is associated with chronic GVHD after HLA-identical haematopoietic stem-cell transplants. Blood. 2007;110(13):4623–4624. doi: 10.1182/blood-2007-08-106385. [DOI] [PubMed] [Google Scholar]
- 43.Tseng LH, Storer B, Petersdorf E, et al. IL10 and IL10 receptor gene variation and outcomes after unrelated and related hematopoietic cell transplantation. Transplantation. 2009;87(5):704–710. doi: 10.1097/TP.0b013e318195c474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Hippen KL, Harker-Murray P, Porter SB, et al. Umbilical cord blood regulatory T-cell expansion and functional effects of tumor necrosis factor receptor family members OX40 and 4-1BB expressed on artificial antigen-presenting cells. Blood. 2008;112(7):2847–2857. doi: 10.1182/blood-2008-01-132951. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Pan LP, Zhang W, Zhang L, et al. CD3Z genetic polymorphism in immune response to hepatitis B vaccination in two independent Chinese populations. PLoS One. 2012;7(4):e35303. doi: 10.1371/journal.pone.0035303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Snelgrove RJ, Cornere MM, Edwards L, et al. OX40 ligand fusion protein delivered simultaneously with the BCG vaccine provides superior protection against murine Mycobacterium tuberculosis infection. J Infect Dis. 2012;205(6):975–983. doi: 10.1093/infdis/jir868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Walch M, Rampini SK, Stoeckli I, et al. Involvement of CD252 (CD134L) and IL-2 in the expression of cytotoxic proteins in bacterial- or viral-activated human T cells. J Immunol. 2009;182(12):7569–7579. doi: 10.4049/jimmunol.0800296. [DOI] [PubMed] [Google Scholar]
- 48.Ozaki K, Ohnishi Y, Iida A, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002;32(4):650–654. doi: 10.1038/ng1047. [DOI] [PubMed] [Google Scholar]
- 49.Bassuny WM, Ihara K, Sasaki Y, et al. A functional polymorphism in the promoter/enhancer region of the FOXP3/Scurfin gene associated with type 1 diabetes. Immunogenetics. 2003;55(3):149–156. doi: 10.1007/s00251-003-0559-8. [DOI] [PubMed] [Google Scholar]
- 50.Reddy CD, Reddy EP. Differential binding of nuclear factors to the intron 1 sequences containing the transcriptional pause site correlates with c-myb expression. Proc Natl Acad Sci U S A. 1989;86(19):7326–7330. doi: 10.1073/pnas.86.19.7326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Ogata K, Morikawa S, Nakamura H, et al. Solution structure of a specific DNA complex of the Myb DNA-binding domain with cooperative recognition helices. Cell. 1994;79(4):639–648. doi: 10.1016/0092-8674(94)90549-5. [DOI] [PubMed] [Google Scholar]
- 52.Fahl SP, Crittenden RB, Allman D, Bender TP. c-Myb is required for pro-B cell differentiation. J Immunol. 2009;183(9):5582–5592. doi: 10.4049/jimmunol.0901187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Golay J, Cusmano G, Introna M. Independent regulation of c-myc, B-myb, and c-myb gene expression by inducers and inhibitors of proliferation in human B lymphocytes. J Immunol. 1992;149(1):300–308. [PubMed] [Google Scholar]
- 54.Quintana AM, Liu F, O’Rourke JP, Ness SA. Identification and regulation of c-Myb target genes in MCF-7 cells. BMC Cancer. 2011;11:30. doi: 10.1186/1471-2407-11-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Jacobson P, Uberti J, Davis W, Ratanatharathorn V. Tacrolimus: a new agent for the prevention of graft-versus-host disease in hematopoietic stem cell transplantation. Bone Marrow Transplant. 1998;22(3):217–225. doi: 10.1038/sj.bmt.1701331. [DOI] [PubMed] [Google Scholar]
- 56.Marmont AM, Horowitz MM, Gale RP, et al. T-cell depletion of HLA-identical transplants in leukemia. Blood. 1991;78(8):2120–2130. [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.